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Computes the mean relative error by normalizing with the given values.

Inherits From: `Mean`

, `Metric`

, `Layer`

, `Module`

tf.keras.metrics.MeanRelativeError( normalizer, name=None, dtype=None )

This metric creates two local variables, `total`

and `count`

that are used to compute the mean relative error. This is weighted by `sample_weight`

, and it is ultimately returned as `mean_relative_error`

: an idempotent operation that simply divides `total`

by `count`

.

If `sample_weight`

is `None`

, weights default to 1. Use `sample_weight`

of 0 to mask values.

Args | |
---|---|

`normalizer` | The normalizer values with same shape as predictions. |

`name` | (Optional) string name of the metric instance. |

`dtype` | (Optional) data type of the metric result. |

m = tf.keras.metrics.MeanRelativeError(normalizer=[1, 3, 2, 3]) m.update_state([1, 3, 2, 3], [2, 4, 6, 8])

# metric = mean(|y_pred - y_true| / normalizer) # = mean([1, 1, 4, 5] / [1, 3, 2, 3]) = mean([1, 1/3, 2, 5/3]) # = 5/4 = 1.25 m.result().numpy() 1.25

Usage with `compile()`

API:

model.compile( optimizer='sgd', loss='mse', metrics=[tf.keras.metrics.MeanRelativeError(normalizer=[1, 3])])

`reset_states`

reset_states()

Resets all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

`result`

result()

Computes and returns the metric value tensor.

Result computation is an idempotent operation that simply calculates the metric value using the state variables.

`update_state`

update_state( y_true, y_pred, sample_weight=None )

Accumulates metric statistics.

Args | |
---|---|

`y_true` | The ground truth values. |

`y_pred` | The predicted values. |

`sample_weight` | Optional weighting of each example. Defaults to 1. Can be a `Tensor` whose rank is either 0, or the same rank as `y_true` , and must be broadcastable to `y_true` . |

Returns | |
---|---|

Update op. |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/keras/metrics/MeanRelativeError